Publications

Elena Congeduti, Frans A. Oliehoek. A Cross-Field Review of State Abstraction for Markov Decision Processes. In Proceedings of Benelux Conference on Artificial Intelligence (BNAIC)/Belgian Dutch Conference on Machine Learning (Benelearn), November 2022.

Victoria Catalan Pastor, Elena Congeduti, Aleksander Czechowski, Frans A. Oliehoek. Overcoming Traffic Sensors Malfunctions with Deep Learning. In Proceedings of Benelux Conference on Artificial Intelligence (BNAIC)/Belgian Dutch Conference on Machine Learning (Benelearn), November 2022.

Miguel Suau, Jinke He, Elena Congeduti, Rolf A. N. Starre, Aleksander Czechowski, Frans A. Oliehoek. Influence-aware Memory Architectures for Deep Reinforcement Learning in POMDPs. In Neural Computing and Applications (NCAA), 2022.

Rolf A. N. Starre, Marco Loog, and Frans A. Oliehoek. An Analysis of Abstracted Model-Based Reinforcement Learning. arXiv preprint arXiv:2208.14407 (2022).

Miguel Suau, Jinke He, Matthijs T.J. Spaan, and Frans A. Oliehoek. Influence-Augmented Local Simulators: a Scalable Solution for Fast Deep RL in Large Networked Systems. In Proceedings of the 39th International Conference on Machine Learning (ICML), July 2022.

Aleksander Czechowski and Georgios Piliouras. 2022. Poincaré-Bendixson Limit Sets in Multi-Agent Learning. In Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), Online, May 9–13, 2022, IFAAMAS, 9 pages (best paper runner-up).

Jinke He, Miguel Suau, Hendrik Baier, Michael Kaisers, Frans A. Oliehoek. Online Planning in POMDPs with Self-Improving Simulators. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), pp. 4628-4634, July 2022.

Miguel Suau, Jinke He, Matthijs T.J. Spaan, and Frans A. Oliehoek. Speeding up Deep Reinforcement Learning through Influence-Augmented Local Simulators. In Proceedings of the Twenty-First International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. , May 2022.

Mustafa Mert Celikok, Frans A. Oliehoek, and Samuel Kaski. Best-Response Bayesian Reinforcement Learning with BA-POMDPs for Centaurs. In Proceedings of the Twenty-First International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2022.

Sammie Katt, Hai Nguyen, Frans A. Oliehoek, and Christopher Amato. BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs. In Proceedings of the Twenty-First International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2022.

Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, and Max Welling. Multi-Agent MDP Homomorphic Networks. In International Conference on Learning Representations, april 2022.

Vibhav Kedege, Aleksander Czechowski, Ludo Stellingwerff, and Frans A. Oliehoek. Multi Robot Surveillance and Planning in Limited Communication Environments. Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, pp. 139--147, February 2022.

Nele Albers, Miguel Suau, and Frans A. Oliehoek. Using Bisimulation Metrics to Analyze and Evaluate Latent State Representations. In Proceedings of the 33rd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), pp. 320–334, November 2021.

Jacopo Castellini, Sam Devlin, Frans A. Oliehoek, and Rahul Savani. Difference Rewards Policy Gradients. In Proceedings of the AAMAS Workshop on Adaptive Learning Agents (ALA), May 2021. Best paper award.

Jacopo Castellini, Sam Devlin, Frans A. Oliehoek, and Rahul Savani. Difference Rewards Policy Gradients. In Proceedings of the Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1475–1477, May 2021.

Shi Yuan Tang, Athirai A. Irissappane, Frans A. Oliehoek, and Jie Zhang. Learning Complex Policy Distribution with CEM Guided Adversarial Hypernetwork. In Proceedings of the Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1308–1316, May 2021.

Jacopo Castellini, Frans A. Oliehoek, Rahul Savani, and Shimon Whiteson. Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning. Autonomous Agents and Multi-Agent Systems, 35(25), June 2021.

Elena Congeduti, Alexander Mey, and Frans A. Oliehoek. Loss Bounds for Approximate Influence-Based Abstraction. In Proceedings of the Twenty International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2021.

Alexander Mey and Frans A. Oliehoek. Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?. In Proceedings of the Twentieth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 23–27, May 2021. Blue Sky Track. Special Mention.

Aleksander Czechowski. Constraint Propagation and Reverse Multi-Agent Learning. AAAI Spring Symposium Series: Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL), March 2021.

Christian Neumeyer, Frans A. Oliehoek, and Dariu Gavrila. General-Sum Multi-Agent Continuous Inverse Optimal Control. IEEE Robotics and Automation Letters, 6(2):3429–3436, IEEE, 2021.

Frans A. Oliehoek, Stefan Witwicki, and Leslie P. Kaelbling. A Sufficient Statistic for Influence in Structured Multiagent Environments. Journal of Artificial Intelligence Research, pp. 789–870, AI Access Foundation, Inc., February 2021.

Aleksander Czechowski and Frans A. Oliehoek. Decentralized MCTS via Learned Teammate Models. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 81--88, January 2021.

Elise Van der Pol, Daniel E. Worrall, Herke Van Hoof, Frans A. Oliehoek, and Max Welling. MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. In Advances in Neural Information Processing Systems 33, pp. 4199–4210, December 2020.

Jinke He, Miguel Suau, and Frans A. Oliehoek. Influence-Augmented Online Planning for Complex Environments. Advances in Neural Information Processing Systems, Dec 2020. [arxiv] [code]

Mikko Lauri and Frans A. Oliehoek. Multi-agent active perception with prediction rewards. In Advances in Neural Information Processing Systems 33, pp. 13651–13661, December 2020.

Wook Lee and Frans A. Oliehoek. Analog Circuit Design with Dyna-Style Reinforcement Learning. To appear at NeurIPS 2020 Workshop on Machine Learning for Engineering Modeling, Simulation, and Design, December 2020.

Miguel Suau, Elena Congeduti, Jinke He, Rolf A. N. Starre, Aleksander Czechowski, Frans A. Oliehoek. Influence-aware Memory for Deep Reinforcement Learning in POMDPs. In NeurIPS'20 Workshop on Deep Reinforcement Learning, December 2020.

Alex Mandersloot, Frans A. Oliehoek and Aleksander Czechowski. Exploring the effects of conditioning Independent Q-Learners on the Sufficient Statistic for Dec-POMDPs. In Proceedings of the 32nd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), abstract, November 2020.

Aleksander Czechowski and Frans A. Oliehoek. Alternating Maximization with Behavioral Cloning. In Proceedings of the 32nd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), abstract, November 2020.

Yaniv Oren, Rolf A. N., Starre, and Frans A. Oliehoek. Comparing Exploration Approaches in Deep Reinforcement Learning for Traffic Light Control. In Proceedings of the 32nd Benelux Conference on Artificial Intelligence (BNAIC) and the 29th Belgian Dutch Conference on Machine Learning (Benelearn), pp. 179-193 November 2020.

Elise Van der Pol, Thomas Kipf, Frans A. Oliehoek, and Max Welling. Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. In Proceedings of the Nineteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1431–1439, May 2020.

Yash Satsangi, Sungsu Lim Lim, Shimon Whiteson, Frans A. Oliehoek, and Martha White. Maximizing Information Gain in Partially Observable Environments via Prediction Rewards. In Proceedings of the Nineteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1215–1223, May 2020.

Jacopo Castellini, Frans A. Oliehoek, Rahul Savani, and Shimon Whiteson. The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. In Proceedings of the Eighteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2019.

Sammie Katt, Frans A. Oliehoek, and Christopher Amato. Bayesian Reinforcement Learning in Factored POMDPs. In Proceedings of the Eighteenth International Conference on Autonomous Agents and Multiagent
Systems (AAMAS)
, May 2019.

Miguel Suau de Castro, Elena Congeduti, Rolf A.N. Starre, Aleksander Czechowski, and Frans A. Oliehoek. Influence-Based Abstraction in Deep Reinforcement Learning. In Proceedings of the AAMAS Workshop on Adaptive Learning Agents (ALA), May 2019.

Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise van der Pol, and Roderich Gross. Beyond Local Nash Equilibria for Adversarial Networks. In Proceedings of the 27th Annual Machine Learning Conference of Belgium and the Netherlands (Benelearn), November 2018.

Frans A. Oliehoek. Interactive Learning and Decision Making: Foundations, Insights & Challenges. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), pp. 5703–5708, July 2018.